Incorrect DISTINCT and ORDER BY Results with RANDOM() in SQLite Queries

Understanding Non-Deterministic Function Behavior in Aggregate Queries

Problem Manifestation: Inconsistent Sorting and Deduplication with RANDOM()

The core issue revolves around unexpected results when combining SQLite’s RANDOM() function with DISTINCT and ORDER BY clauses. Users observed that queries producing randomized values failed to sort correctly or eliminate duplicates when using ascending order, while descending order worked as expected. For example:

WITH RECURSIVE cnt(x) AS (SELECT 1 UNION ALL SELECT x+1 FROM cnt LIMIT 1000)
SELECT DISTINCT abs(random())%10 AS r FROM cnt ORDER BY r ASC;

This query might return unsorted results or retain duplicates despite DISTINCT. However, changing ORDER BY r DESC often resolved the issue. Similar anomalies appeared in aggregate queries using GROUP BY with random values, where counts mismatched expected distributions due to multiple RANDOM() evaluations per row.

Key Observations:

1. Order Sensitivity: Ascending vs. descending sorting produced different outcomes.
2. Non-Deterministic Aggregation: COUNT(*) with GROUP BY RANDOM()%N yielded unstable groupings.
3. DISTINCT Failure: DISTINCT occasionally failed to remove duplicates when combined with RANDOM().

Root Causes: Volatile Functions and Query Optimization

1. Volatility of RANDOM() and Multiple Evaluations

RANDOM() is a volatile function that returns a new value on every invocation. When used in expressions like abs(random())%10, SQLite may evaluate it multiple times during different query processing stages:

• During Row Generation: When producing base rows (e.g., via generate_series or recursive CTEs).
• During Projection: When calculating column values for the final result set.
• During Sorting/Deduplication: When applying ORDER BY or DISTINCT.

This multi-phase evaluation causes the same logical row to have different RANDOM() values at different stages. For instance:

• A row might generate r=5 during projection but r=3 during sorting.
• DISTINCT operates on the projected value, while ORDER BY might use a re-evaluated value, leading to mismatches.

2. Query Optimizer Assumptions

SQLite’s optimizer assumes deterministic expressions. When non-deterministic functions like RANDOM() are involved, the optimizer’s decisions (e.g., pushing predicates, reordering operations) can inadvertently cause repeated evaluations. For example:

• Early Sorting: If the optimizer sorts data before applying DISTINCT, the RANDOM() values used for sorting differ from those in the final projection.
• Lazy Column Evaluation: Columns in ORDER BY might be re-evaluated during sorting instead of reusing projected values.

3. UNION ALL vs. UNION and Temporary Storage

The choice between UNION ALL and UNION impacts whether intermediate results are stored. UNION requires deduplication, forcing SQLite to retain rows for comparison. This storage can lead to RANDOM() re-evaluation if the optimizer does not materialize results correctly. However, the primary issue here is not storage but the timing of RANDOM() calls.

Resolving Inconsistent Results: Strategies and Solutions

1. Using Statement-Stable Randomness with stmtrand()

A fix introduced in SQLite’s group-by-consistency branch adds the stmtrand(seed) function, which generates reproducible per-statement values. Unlike RANDOM(), stmtrand() yields the same sequence of values within a single SQL statement, ensuring consistency across query phases.

Example Fix:

WITH RECURSIVE cnt(x) AS (VALUES(1) UNION ALL SELECT x+1 FROM cnt WHERE x<10)
SELECT count(*), stmtrand(1)%3 AS r FROM cnt GROUP BY r ORDER BY count(*);

This produces stable groupings because stmtrand() is evaluated once per row and reused during aggregation and sorting.

Implementation Notes:

• Seed Value: The seed parameter initializes the sequence. Different seeds produce different sequences.
• Determinism: Results are reproducible for the same statement and seed, aiding debugging.

2. Materializing Intermediate Results

Force SQLite to materialize results before sorting/deduplication using subqueries or CTEs:

SELECT DISTINCT r FROM (
  SELECT abs(random())%10 AS r FROM generate_series(1,20)
) ORDER BY r ASC;

This ensures RANDOM() is evaluated once per row during the subquery, with sorting applied to the materialized results.

3. Avoiding Non-Deterministic Functions in Sorting/Deduplication

Redesign queries to avoid relying on non-deterministic values for sorting or deduplication. If randomness is required, precompute values in a temporary table:

CREATE TEMP TABLE temp_r AS SELECT abs(random())%10 AS r FROM generate_series(1,20);
SELECT DISTINCT r FROM temp_r ORDER BY r ASC;

4. Compiling SQLite with Math Functions Enabled

The MOD() function might be missing if SQLite is compiled without -DSQLITE_ENABLE_MATH_FUNCTIONS. To enable it:

1. For Command-Line Compilation:

   gcc -DSQLITE_ENABLE_MATH_FUNCTIONS -o sqlite3 shell.c sqlite3.c
   

2. In Prebuilt Binaries: Verify that your SQLite distribution includes math functions or recompile with the flag.

5. Integrating Fixed SQLite into C# Applications

To use the patched SQLite in .NET:

1. Replace SQLite.Interop.dll:
   • Compile SQLite as a DLL with -DSQLITE_ENABLE_MATH_FUNCTIONS.
   • Overwrite the existing SQLite.Interop.dll in your project’s output directory.
2. Use Native Library Preloading:

   SQLitePCL.Batteries.Init(new NativeLibraryAdapter("path/to/custom_sqlite3.dll"));
   

6. Verifying Query Execution Plans

Use EXPLAIN to analyze when RANDOM() is evaluated:

EXPLAIN SELECT DISTINCT abs(random())%10 AS r FROM generate_series(1,20) ORDER BY r ASC;

Look for multiple Function opcodes for RANDOM(), indicating re-evaluation.

Summary of Critical Fixes and Best Practices

• Use stmtrand() for reproducible randomness in aggregates and sorting.
• Materialize volatile expressions in subqueries before further processing.
• Recompile SQLite with necessary flags if functions like MOD() are missing.
• Avoid mixing non-deterministic functions with DISTINCT or ORDER BY unless results are materialized.

This guide addresses the intricacies of SQLite’s handling of non-deterministic functions and provides actionable solutions to ensure reliable query results.